During manufacturing processes numerous operations may occur, each of these operations affecting quality of the final product. In order to ensure quality during such manufacturing processes techniques have been developed to monitor the processes and to enable data obtained during such monitoring to be analyzed to determine whether certain quality thresholds were or are being met.
For example, light emissions from processes can be captured to provide data regarding a quality state of a process by using photo-sensitive devices that are sensitive to different wavelength ranges of the electromagnetic spectrum (e.g. visible, near infrared, infrared). Alternatively, or in addition, a filter can be placed in front of the device in order to selectively block certain wavelengths while allowing certain other wavelengths for analysis to pass to the sensing device. For example, this may be done where a particular phenomenon is visible at a specific wavelength. In addition, it may be desirable to block out wavelengths that may damage the sensing device.
The light emissions detected by the sensor can reveal different phenomenon and artifacts that can be linked to different quality features.
In laser welding for example, pyrometers can be used to capture point data over time and are available from, for example, Precitec, Plasmo, and 4D currently have commercialized solutions on the market. Further, image capture devices such as CMOS and CCD cameras are commonly used to gather two dimensional data regarding the geometry and features of a weld pool and process zone, with, for example, Precitec and Prometec currently providing commercialized solutions.
Active illumination by lasers or light emitting diodes may be used in addition to image capture devices for reasons such as enhancing the image capture capability, and enabling lower exposure times and faster frame rates. For example, Lessmueller and Cavitar provide solutions suitable for such uses.
More recently there has been growth in multi-sensor, single-sensor multi-wavelength techniques, and monitoring of specific wavelengths/bandwidths to capture phenomenon that can generally only be reliably detected at specific wavelengths.
Regardless of the image captured and the technique used, some form of identification and quantification of the artifacts in the images is required in order to implement quality control. The system from Precitec for example comes with two standard packages, ROI (regions of interest) and Qualas (based on thresholding).
Post-process inspection can also be performed using methods such as ultrasound, eddy current and thermography analyses. However, the need remains for a robust in-situ/inline monitoring method enabling the elimination of additional post-processing inspection.
Further, existing in-situ/inline monitoring techniques typically use a single image, and processing thereof, to determine, subjectively, the quality of a process at a given time in the process, and this determination may then be used to evaluate the quality of the process and/or to control the process.
U.S. Pat. No. 7,129,438 to Miyachi discloses the use of algorithms to extract single variable output from two-dimensional images of pulsed laser welding processes, noting that the systems on the market at that time did not offer sufficient image processing capability.
WO 2011/120672 teaches that process images can be used for the control of laser power in welding processes by decision making based on the presence of lack of image artifacts, in particular a full penetration keyhole.
Such approaches present problems, however. For example, in some dynamic processes, it is not necessarily the occurrence of a single defective state in a process that can cause a defect in the product, but rather the time over which the process operates in a defective state. In other words, analysis of the temporal and spatial distribution of defective states within the process or within a given portion of the process can better define the quality of the product.
In addition, other phenomenon occurring during the process (e.g. gas emissions, light irradiated by gas emissions that block viewing of the process phenomenon), and even dynamic random or periodic oscillation between states may cloud or block reliable imaging and fair judgment of the actual process state, and may therefore introduce difficulties on an image by image basis.
In such situations, and also especially for sensitive operations or parts, it may be preferred to not hand over power control to a closed loop system, and is instead preferable to implement a highly robust statistical analysis for monitoring, correction, and control approach based on processing of batches of images.
It is accordingly a primary object of the disclosure to provide systems and methods for overcoming the deficiencies of the currently available systems and methods by using temporal discretization of process analysis.